Novel Computation Methods for the Analysis of Cell-Free DNA Sequence Data

项目摘要 无细胞DNA（cfDNA）的非侵入性检测有望影响广泛的临床治疗方案。 疾病，例如产前状况、癌症、移植、自身免疫性疾病、创伤和心血管疾病 疾病虽然这一领域正在成为最有前途和最令人兴奋的医学领域之一，但很少有人 生物信息学工具可用于促进从cfDNA测序数据中提取信息，尽管 cfDNA数据具有许多独特的性质。在这个提议中，我们的目标是生成一套计算 方法促进cfDNA测序数据的分析和解释，并证明其在 癌症检测和表征。具体地说，我们将开发以下计算方法 应用：（1）使用cfDNA甲基化组超灵敏地检测和定位多种类型的癌症;（2）检测 cfDNA测序数据中的拷贝数变异（CNV）; cfDNA测序数据。这些计算工具将用从队列收集的cfDNA样品进行验证。 参与免疫治疗临床试验的肺癌患者的血液样本库， 不同类型的癌症和一组肝癌患者。虽然我们把癌症作为主要的背景 为了开发这些应用，许多方法可以适用于其他疾病，例如产前诊断 和器官移植监测。我们预计，上述开源工具将大大促进cfDNA- 疾病诊断和监测。 1

Project Summary Non-invasive detection of cell-free DNA(cfDNA) promises to impact clinical regimens of a wide range of diseases, e.g. prenatal conditions, cancer, transplantation, autoimmune disease, trauma, and cardiovascular disease. While this field is emerging as one of the most promising and exciting areas of medicine, few bioinformatics tools are available to facilitate the information extraction from cfDNA sequencing data, although cfDNA data possesses many unique properties. In this proposal, we aim to generate a suite of computational methods facilitating the analysis and interpretation of cfDNA sequencing data, and demonstrate its utilities in cancer detection and characterization. Specifically, we will develop computational methods for the following applocations: (1) Ultra-sensitively detect and locate multiple types of cancer using cfDNA methylome; (2) Detect Copy Number Variation (CNV) in cfDNA sequencing data; (3) Annotate Single Nucleotide Variations (SNV) in cfDNA sequencing data. These computational tools will be validated with cfDNA samples collected from a cohort of lung cancer patients participating in an immunotherapy clinical trial, a repository of blood samples from patients with different types of cancer, and a cohort of liver cancer patients. Although we use cancer as the main context for developing these applications, many of the methods can be adapted to other diseases, e.g. prenatal diagnosis and organ transplant monitoring. We expect that the above open-source tools will significantly facilitate cfDNA- based disease diagnosis and monitoring. 1